Upgrade the weights loading method to avoid duplicated loading.

app.py

@@ -49,23 +49,23 @@ weights_path = 'General'
 birefnet = AutoModelForImageSegmentation.from_pretrained('/'.join(('zhengpeng7', usage_to_weights_file[weights_path])), trust_remote_code=True)
 birefnet.to(device)
 birefnet.eval()
-birefnet.weights_path = weights_path
+weights_path = weights_path
 
 
 @spaces.GPU
 def predict(image, resolution, weights_file):
-    global birefnet
-    if birefnet.weights_path != weights_file:
+    global weights_path
+    if weights_path != weights_file:
         print('*' * 10)
-        print('\t1: ', weights_file, birefnet.weights_path)
+        print('\t1: ', weights_file, weights_path)
         # Load BiRefNet with chosen weights
         _weights_file = '/'.join(('zhengpeng7', usage_to_weights_file[weights_file] if weights_file is not None else 'BiRefNet'))
         print('Change weights to:', _weights_file)
         birefnet = birefnet.from_pretrained(_weights_file)
         birefnet.to(device)
         birefnet.eval()
-        birefnet.weights_path = weights_file
-        print('\t2: ', weights_file, birefnet.weights_path)
+        weights_path = weights_file
+        print('\t2: ', weights_file, weights_path)
 
     resolution = f"{image.shape[1]}x{image.shape[0]}" if resolution == '' else resolution
     # Image is a RGB numpy array.